Supplemental Methods Structure and logic Gene2Function (G2F) was built using php, python and javascript with a backend MySQL database. Jquery and Silex framework libraries were used for software development. To respond to the Ajax requests for mine data, a Python script was developed with the InterMine webservice package to query data from the various InterMine sources. The G2F site is hosted by the Harvard Medical School Research Computing group. See also Fig. 1 and Table 1 in the main text. The core coding components and documentation of the data structure, as well as instructions for setting up the database, are available on github (https://github.com/DRSC- FG/gene2function). Information sources Orthologs. Ortholog mapping is based on the DRSC Integrative Ortholog Prediction Tool (DIOPT), originally published in 2011 (HU et al. 2011) and regularly updated to cover more organisms, to integrate more ortholog prediction algorithms, and to synchronize with new genome annotations (HU et al. 2017). Currently, DIOPT integrates 14 different ortholog prediction algorithms for 9 major model organisms and calculate a simple voting score to reflect the strength of orthologous relationships. Gene2Function display the orthologous relationships supported by at least 3 algorithms and only display weak associations (score 1 or 2) when there is no better option. Besides the voting score, DIOPT also provides pairwise and multiple sequence alignments for G2F. Human disease-gene associations. Mapping of human diseases to associated genes is done based on our DIOPT-Diseases and Traits (DIOPT-DIST) approach (HU et al. 2011). Two types of disease-gene associations are included in the results; first, gene-disease associations annotated at OMIM (https://omim.org/downloads/), and second, reported gene- disease or gene-trait associations from the catalog of genome-wide association studies (GWAS Catalog, https://www.ebi.ac.uk/gwas/docs/file-downloads). Detailed gene information pages. G2F provides links to detailed, expert-curated information pages for individual genes in human or model organism databases (MODs). The following sources were used: for human genes, the Human Gene Nomenclature Committee (HGCN) resource (genenames.org/) (YATES et al. 2017); for mouse genes, the Mouse Genome Database (MGD) resource (informatics.jax.org/) (BLAKE et al. 2017); rat, the Rat Genome Database (RGD) (rgd.mcw.edu/) (SHIMOYAMA et al. 2015); frog, Xenbase (xenbase.org) (KARPINKA et al. 2015); zebrafish, ZFIN (zfin.org) (HOWE et al. 2017); for Drosophila, FlyBase (flybase.org) (GRAMATES et al. 2017); for C. elegans, WormBase (wormbase.org) (HOWE et al. 2016); for S. cerevisiae, the Saccharomyces Genome Database (SGD) (yeastgenome.org/) (CHERRY et al. 2012); for S. pombe, PomBase (pombase.org) (MCDOWALL et al. 2015). Gene ontology terms. Gene ontology (GO) terms were downloaded from NCBI ftp site (ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene2go.gz). A java program was developed to parse the file, select the records relevant to the 9 species covered by G2F, and format the file for database upload. A script is scheduled to run monthly to automatically update the database. Only gene ontology assignments based on experimental evidence are selected and displayed at G2F; these include assignments based on the evidence codes EXP, IDA, IPI, IMP, IGI and IEP. PubMed publications. Gene-associated publications are retrieved from NCBI ftp site (ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene2pubmed.gz). A java program was developed to parse the file, select the records relevant to the 9 species covered by G2F, and format the file for database upload. A script is scheduled to run monthly to automatically update the database. At the user-interface, the Pubmed IDs are sorted in descending order so that more recent publications appear at the top. We filtered out publications with >100 associated genes so that the publication counts reflect the type of low- to medium-throughput studies most likely to include functional characterization. Protein and genetic interaction annotations. Protein-protein interaction and genetic interaction annotations are from BioGrid (https://thebiogrid.org/) (CHATR-ARYAMONTRI et al. 2017). Phenotype and expression annotations. Phenotype and expression annotations for human, mouse, zebrafish, and Drosophila genes are retrieved using application program interfaces (APIs) provided by InterMine (http://intermine.org/) (SMITH et al. 2012). The phenotype and expression annotations for worm and budding yeast genes are provided by direct link out to MODs (CHERRY et al. 2012; HOWE et al. 2016), whereas expression data for fission yeast genes are retrieved from PomBase (MCDOWALL et al. 2015) and stored locally. Disruption phenotype and 3D structure annotation. Disruption phenotype and 3D structure annotation are queried from UniProt website directly (http://www.uniprot.org/) (UNIPROT CONSORTIUM 2017). The information were processed, formatted and stored locally, which is subjected to periodically update. Reagent information. Open reading frame (ORF) clone information was obtained from the Dana Farber/Harvard Cancer Center DNA Resource Core PlasmID database (https://plasmid.med.harvard.edu/PLASMID/) (ZUO et al. 2007). We include in the G2F report only ORFs in gateway vectors, majority of which were from the ORFeome collaboration (OC) consortium (COLLABORATION 2016). In addition to genome-scale human ORF clones, large- scale collections for several other model organisms (mouse, Drosophila, and the yeast S. cerevisiae) are also included in G2F. GWAS gene function analysis To generate Table S1, we first retrieved genes from the NHGRI-EBI GWAS Catalog th (Feb 27 2017) (MACARTHUR et al. 2017) that we have stored locally at DIOPT-DIST (HU et al. 2011). Genes associated with traits that are not categorized as diseases, such as normal phenotypes, disease risk factors, diagnosis, and treatment, were filtered out. For the remaining 6131 genes, we surveyed publication counts and GO annotations. 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